---
title: TigerGraph
---

>[TigerGraph](https://www.tigergraph.com/tigergraph-db/) is a natively distributed and high-performance graph database.
> The storage of data in a graph format of vertices and edges leads to rich relationships,
> ideal for grouding LLM responses.

A big example of the `TigerGraph` and `LangChain` integration [presented here](https://github.com/tigergraph/graph-ml-notebooks/blob/main/applications/large_language_models/TigerGraph_LangChain_Demo.ipynb).

## Installation and Setup

Follow instructions [how to connect to the `TigerGraph` database](https://docs.tigergraph.com/pytigergraph/current/getting-started/connection).

Install the Python SDK:

<CodeGroup>
```bash pip
pip install pyTigerGraph
```

```bash uv
uv add pyTigerGraph
```
</CodeGroup>

## Example

To utilize the `TigerGraph InquiryAI` functionality, you can import `TigerGraph` from `langchain_community.graphs`.

```python
import pyTigerGraph as tg

conn = tg.TigerGraphConnection(host="DATABASE_HOST_HERE", graphname="GRAPH_NAME_HERE", username="USERNAME_HERE", password="PASSWORD_HERE")

### ==== CONFIGURE INQUIRYAI HOST ====
conn.ai.configureInquiryAIHost("INQUIRYAI_HOST_HERE")

from langchain_community.graphs import TigerGraph

graph = TigerGraph(conn)
result = graph.query("How many servers are there?")
print(result)
```
